Version 1
: Received: 10 September 2024 / Approved: 11 September 2024 / Online: 11 September 2024 (12:39:30 CEST)
How to cite:
LI, Y.; LU, T.; ZENG, G.; ZHAO, K.; PENG, S. Advanced ENF Region Classification Using UniTS-SinSpec: A Novel Approach Integrating Sinusoidal Activation Function and Spectral Attention. Preprints2024, 2024090897. https://doi.org/10.20944/preprints202409.0897.v1
LI, Y.; LU, T.; ZENG, G.; ZHAO, K.; PENG, S. Advanced ENF Region Classification Using UniTS-SinSpec: A Novel Approach Integrating Sinusoidal Activation Function and Spectral Attention. Preprints 2024, 2024090897. https://doi.org/10.20944/preprints202409.0897.v1
LI, Y.; LU, T.; ZENG, G.; ZHAO, K.; PENG, S. Advanced ENF Region Classification Using UniTS-SinSpec: A Novel Approach Integrating Sinusoidal Activation Function and Spectral Attention. Preprints2024, 2024090897. https://doi.org/10.20944/preprints202409.0897.v1
APA Style
LI, Y., LU, T., ZENG, G., ZHAO, K., & PENG, S. (2024). Advanced ENF Region Classification Using UniTS-SinSpec: A Novel Approach Integrating Sinusoidal Activation Function and Spectral Attention. Preprints. https://doi.org/10.20944/preprints202409.0897.v1
Chicago/Turabian Style
LI, Y., Kai ZHAO and Shufan PENG. 2024 "Advanced ENF Region Classification Using UniTS-SinSpec: A Novel Approach Integrating Sinusoidal Activation Function and Spectral Attention" Preprints. https://doi.org/10.20944/preprints202409.0897.v1
Abstract
The electric network frequency (ENF), often referred to as the industrial heartbeat, plays a crucial role in the power system. In recent years, it has found applications in multimedia evidence identification for court proceedings and audio-visual temporal source identification. This paper introduces a ENF region classification model named UniTS-SinSpec within the UniTS framework. The model integrates the si-nusoidal activation function and spectral attention mechanism while also redesigning the model frame-work. Training is conducted using a public dataset on the open science framework platform (OSF), with final experimental results demonstrating that after parameter optimization, the UniTS-SinSpec model achieves an average validation accuracy of 97.47%, surpassing current state-of-the-art and baseline models. Accurate classification can significantly aid in ENF temporal source identification. Future re-search will focus on expanding dataset coverage and diversity to verify the model's generality and ro-bustness across different regions, time spans, and data sources. Additionally, it aims to explore extensive application potential of ENF region classification in preventing crimes such as telecommunications fraud, terrorism, and child pornography.
Keywords
electric network frequency; ENF classification; sinusoidal activation function; spectral attention; UniTS-SinSpec model
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
